Abstract: Analogies among entities may be detected by obtaining associative counts among the entities and computing similarity measures among given entities and other entities, using the associative counts. First and second entities are then identified as being analogies if the first entity has a strongest similarity measure with respect to the second entity and the second entity also has a strongest similarity measure with respect to the first entity. The similarity measures may be calculated using a normalized entropy inverted among a given entity and other entities.

Abstract: A location of a missing object is predicted based on past sightings of objects including the missing object, and a new sighting of the objects except for the missing object. For a respective given object in the objects, the past sightings are memorized based on respective distances of respective remaining objects from the respective given object. Distance-based memorization may take place using an agent or associative memory for a respective given object. Then, for a respective given object, except for the missing object, a distance of the missing object from the respective given object is predicted, based on the past sightings that have been memorized and the new sighting, to obtain candidate locations for the missing object. The candidate locations are then disambiguated, to predict the location of the missing object.

Abstract: Analogies among entities may be detected by obtaining associative counts among the entities and computing similarity measures among given entities and other entities, using the associative counts. First and second entities are then identified as being analogies if the first entity has a strongest similarity measure with respect to the second entity and the second entity also has a strongest similarity measure with respect to the first entity. The similarity measures may be calculated using a normalized entropy inverted among a given entity and other entities.

Abstract: Analogies among entities may be detected by obtaining associative counts among the entities and computing similarity measures among given entities and other entities, using the associative counts. First and second entities are then identified as being analogies if the first entity has a strongest similarity measure with respect to the second entity and the second entity also has a strongest similarity measure with respect to the first entity. The similarity measures may be calculated using a normalized entropy inverted among a given entity and other entities.

Abstract: Analogies among entities may be detected by obtaining associative counts among the entities and computing similarity measures among given entities and other entities, using the associative counts. First and second entities are then identified as being analogies if the first entity has a strongest similarity measure with respect to the second entity and the second entity also has a strongest similarity measure with respect to the first entity. The similarity measures may be calculated using a normalized entropy inverted among a given entity and other entities.

Abstract: An artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. A respective dendrite includes a respective power series of weights. The weights in a given power of the power series represent a maximal projection. A respective power also may include at least one switch, to identify holes in the projections. By providing maximal projections, linear scaling may be provided for the maximal projections, and quasi-linear scaling may be provided for the artificial neuron, while allowing a lossless compression of the associations. Accordingly, hetero-associative and/or auto-associative recall may be accommodated for large numbers of inputs, without requiring geometric scaling as a function of input.

Abstract: An artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. A respective dendrite includes a respective power series of weights. The weights in a given power of the power series represent a maximal projection. A respective power also may include at least one switch, to identify holes in the projections. By providing maximal projections, linear scaling may be provided for the maximal projections, and quasi-linear scaling may be provided for the artificial neuron, while allowing a lossless compression of the associations. Accordingly, hetero-associative and/or auto-associative recall may be accommodated for large numbers of inputs, without requiring geometric scaling as a function of input.